Look-ahead energy efficient VM allocation approach for data centers


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Caglar I., Altılar D. T.

JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS, vol.11, no.1, 2022 (Journal Indexed in SCI) identifier

  • Publication Type: Article / Article
  • Volume: 11 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.1186/s13677-022-00281-x
  • Title of Journal : JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS
  • Keywords: Cloud computing, Resource management, Energy efficiency, VIRTUAL MACHINES, CLOUD, MIGRATION, POWER, CONSOLIDATION, CONSUMPTION, MANAGEMENT, HEURISTICS, COST

Abstract

Energy efficiency is an important issue for reducing environmental dissipation. Energy efficient resource provisioning in cloud environments is a challenging problem because of its dynamic nature and varied application workload characteristics. In the literature, live migration of virtual machines (VMs) among servers is commonly proposed to reduce energy consumption and to optimize resource usage, although it comes with essential drawbacks, such as migration cost and performance degradation. Energy efficient provisioning is addressed at the data center level in this research. A novel efficient resource management algorithm for virtualized data centers that optimizes the number of servers to meet the requirements of dynamic workloads without migration is proposed in this paper. The proposed approach, named Look-ahead Energy Efficient VM Allocation (LAA), contains a Holt Winters-based prediction module. Energy efficiency and performance are inversely proportional. The energy-performance trade-off relies on periodic comparisons of the predicted and active numbers of servers. To evaluate the proposed algorithm, experiments are conducted with real-world workload traces from Google Cluster. LAA is compared with the best approach provided by CloudSim based on VM migration called Local Regression-Minimum Migration Time (LR-MMT). The experimental results show that the proposed algorithm leads to a consumption reduction of up to 45% to complete one workload compared with the LR-MMT.